Abstract
The problem of finding corresponding points between images of the same scene is at the heart of many computer vision problems. In this paper we present a real-time approach to finding correspondences under changes in scale, rotation, viewpoint and illumination using Simple Circular Accelerated Robust Features (SCARF). Prominent descriptors such as SIFT and SURF find robust correspondences, but at a computation cost that limits the number of points that can be handled on low-memory, low-power devices. Like SURF, SCARF is based on Haar wavelets. However, SCARF employs a novel non-uniform sampling distribution, structure, and matching technique that provides computation times comparable to the state-of-the-art without compromising distinctiveness and robustness. Computing 512 SCARF descriptors takes 12.6ms on a 2.4GHz processor, and each descriptor occupies just 60 bytes. Therefore the descriptor is ideal for real-time applications which are implemented on low-memory, low-power devices.
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Thomas, S.J., MacDonald, B.A., Stol, K.A. (2011). Real-Time Robust Image Feature Description and Matching. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19309-5_26
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DOI: https://doi.org/10.1007/978-3-642-19309-5_26
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